Overview

Dataset statistics

Number of variables54
Number of observations147750
Missing cells1725084
Missing cells (%)21.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.9 MiB
Average record size in memory432.0 B

Variable types

Numeric17
Boolean11
DateTime2
Text7
Unsupported2
Categorical15

Alerts

pedestrian_fl has constant value ""Constant
motor_vehicle_fl has constant value ""Constant
motorcycle_fl has constant value ""Constant
bicycle_fl has constant value ""Constant
other_fl has constant value ""Constant
other_death_count has constant value ""Constant
private_dr_fl has constant value ""Constant
micromobility_fl has constant value ""Constant
crash_fatal_fl is highly imbalanced (94.8%)Imbalance
road_constr_zone_fl is highly imbalanced (70.1%)Imbalance
death_cnt is highly imbalanced (97.7%)Imbalance
apd_confirmed_fatality is highly imbalanced (94.9%)Imbalance
apd_confirmed_death_count is highly imbalanced (97.7%)Imbalance
motor_vehicle_death_count is highly imbalanced (98.8%)Imbalance
bicycle_death_count is highly imbalanced (99.7%)Imbalance
bicycle_serious_injury_count is highly imbalanced (99.0%)Imbalance
pedestrian_death_count is highly imbalanced (98.6%)Imbalance
pedestrian_serious_injury_count is highly imbalanced (98.3%)Imbalance
motorcycle_death_count is highly imbalanced (99.4%)Imbalance
motorcycle_serious_injury_count is highly imbalanced (97.2%)Imbalance
other_serious_injury_count is highly imbalanced (> 99.9%)Imbalance
micromobility_serious_injury_count is highly imbalanced (99.8%)Imbalance
micromobility_death_count is highly imbalanced (99.9%)Imbalance
case_id has 1858 (1.3%) missing valuesMissing
rpt_latitude has 137456 (93.0%) missing valuesMissing
rpt_longitude has 137456 (93.0%) missing valuesMissing
rpt_block_num has 19611 (13.3%) missing valuesMissing
rpt_street_pfx has 67805 (45.9%) missing valuesMissing
rpt_street_sfx has 50340 (34.1%) missing valuesMissing
latitude has 2243 (1.5%) missing valuesMissing
longitude has 2243 (1.5%) missing valuesMissing
street_nbr has 87038 (58.9%) missing valuesMissing
street_name_2 has 81474 (55.1%) missing valuesMissing
street_nbr_2 has 147750 (100.0%) missing valuesMissing
contrib_factr_p1_id has 119143 (80.6%) missing valuesMissing
contrib_factr_p2_id has 143235 (96.9%) missing valuesMissing
pedestrian_fl has 144245 (97.6%) missing valuesMissing
motorcycle_fl has 144148 (97.6%) missing valuesMissing
bicycle_fl has 145306 (98.3%) missing valuesMissing
other_fl has 142905 (96.7%) missing valuesMissing
point has 2243 (1.5%) missing valuesMissing
micromobility_fl has 147439 (99.8%) missing valuesMissing
crash_id has unique valuesUnique
rpt_block_num is an unsupported type, check if it needs cleaning or further analysisUnsupported
street_nbr_2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
crash_sev_id has 4851 (3.3%) zerosZeros
sus_serious_injry_cnt has 143326 (97.0%) zerosZeros
nonincap_injry_cnt has 117724 (79.7%) zerosZeros
poss_injry_cnt has 111987 (75.8%) zerosZeros
non_injry_cnt has 24343 (16.5%) zerosZeros
unkn_injry_cnt has 133316 (90.2%) zerosZeros
tot_injry_cnt has 82717 (56.0%) zerosZeros
motor_vehicle_serious_injury_count has 144928 (98.1%) zerosZeros

Reproduction

Analysis started2024-03-28 20:14:14.073963
Analysis finished2024-03-28 20:15:25.045032
Duration1 minute and 10.97 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

crash_id
Real number (ℝ)

UNIQUE 

Distinct147750
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16810778
Minimum1001
Maximum1.8029054 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:25.100696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile14068914
Q115309291
median16758228
Q318299943
95-th percentile19694364
Maximum1.8029054 × 108
Range1.8028954 × 108
Interquartile range (IQR)2990651.8

Descriptive statistics

Standard deviation1832197.4
Coefficient of variation (CV)0.10898944
Kurtosis427.71533
Mean16810778
Median Absolute Deviation (MAD)1491593
Skewness4.873197
Sum2.4837925 × 1012
Variance3.3569471 × 1012
MonotonicityNot monotonic
2024-03-28T16:15:25.187605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13762420 1
 
< 0.1%
17764668 1
 
< 0.1%
17764654 1
 
< 0.1%
17748180 1
 
< 0.1%
17776625 1
 
< 0.1%
17763093 1
 
< 0.1%
17778984 1
 
< 0.1%
17782080 1
 
< 0.1%
17742842 1
 
< 0.1%
17757259 1
 
< 0.1%
Other values (147740) 147740
> 99.9%
ValueCountFrequency (%)
1001 1
< 0.1%
13756880 1
< 0.1%
13756881 1
< 0.1%
13756928 1
< 0.1%
13756945 1
< 0.1%
13756946 1
< 0.1%
13756947 1
< 0.1%
13756956 1
< 0.1%
13756957 1
< 0.1%
13756958 1
< 0.1%
ValueCountFrequency (%)
180290542 1
< 0.1%
20087189 1
< 0.1%
20085660 1
< 0.1%
20085537 1
< 0.1%
20085478 1
< 0.1%
20085477 1
< 0.1%
20085476 1
< 0.1%
20085475 1
< 0.1%
20085474 1
< 0.1%
20085473 1
< 0.1%

crash_fatal_fl
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size144.4 KiB
False
146884 
True
 
866
ValueCountFrequency (%)
False 146884
99.4%
True 866
 
0.6%
2024-03-28T16:15:25.264163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct144667
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2014-03-26 06:41:00
Maximum2024-03-11 22:05:00
2024-03-28T16:15:25.331094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:25.416307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1440
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2024-03-28 00:00:00
Maximum2024-03-28 23:59:00
2024-03-28T16:15:25.539662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:25.626311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

case_id
Text

MISSING 

Distinct145678
Distinct (%)99.9%
Missing1858
Missing (%)1.3%
Memory size1.1 MiB
2024-03-28T16:15:25.874087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length20
Median length9
Mean length9.0036945
Min length1

Characters and Unicode

Total characters1313567
Distinct characters71
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145464 ?
Unique (%)99.7%

Sample

1st row140890874
2nd row140860852
3rd row140871196
4th row140991015
5th row140971248
ValueCountFrequency (%)
tx 208
 
0.1%
7
 
< 0.1%
go 6
 
< 0.1%
2023 5
 
< 0.1%
23 4
 
< 0.1%
202010169 2
 
< 0.1%
23-945608 2
 
< 0.1%
161290062 2
 
< 0.1%
2019217897 2
 
< 0.1%
c315-0226-005 2
 
< 0.1%
Other values (145707) 145918
99.8%
2024-03-28T16:15:26.233512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 284315
21.6%
0 204676
15.6%
2 187630
14.3%
3 118323
9.0%
4 90263
 
6.9%
5 88855
 
6.8%
6 86587
 
6.6%
9 82968
 
6.3%
7 82780
 
6.3%
8 82526
 
6.3%
Other values (61) 4644
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1308923
99.6%
Dash Punctuation 2701
 
0.2%
Uppercase Letter 1430
 
0.1%
Space Separator 274
 
< 0.1%
Lowercase Letter 215
 
< 0.1%
Other Punctuation 15
 
< 0.1%
Modifier Symbol 7
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 409
28.6%
T 348
24.3%
X 337
23.6%
P 40
 
2.8%
S 30
 
2.1%
G 20
 
1.4%
N 20
 
1.4%
R 20
 
1.4%
D 19
 
1.3%
B 18
 
1.3%
Other values (16) 169
11.8%
Lowercase Letter
ValueCountFrequency (%)
c 26
12.1%
a 25
11.6%
e 20
 
9.3%
n 17
 
7.9%
l 17
 
7.9%
r 13
 
6.0%
i 12
 
5.6%
t 9
 
4.2%
o 9
 
4.2%
d 8
 
3.7%
Other values (14) 59
27.4%
Decimal Number
ValueCountFrequency (%)
1 284315
21.7%
0 204676
15.6%
2 187630
14.3%
3 118323
9.0%
4 90263
 
6.9%
5 88855
 
6.8%
6 86587
 
6.6%
9 82968
 
6.3%
7 82780
 
6.3%
8 82526
 
6.3%
Other Punctuation
ValueCountFrequency (%)
/ 5
33.3%
, 4
26.7%
# 3
20.0%
& 1
 
6.7%
. 1
 
6.7%
* 1
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 2701
100.0%
Space Separator
ValueCountFrequency (%)
274
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1311922
99.9%
Latin 1645
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 409
24.9%
T 348
21.2%
X 337
20.5%
P 40
 
2.4%
S 30
 
1.8%
c 26
 
1.6%
a 25
 
1.5%
G 20
 
1.2%
N 20
 
1.2%
e 20
 
1.2%
Other values (40) 370
22.5%
Common
ValueCountFrequency (%)
1 284315
21.7%
0 204676
15.6%
2 187630
14.3%
3 118323
9.0%
4 90263
 
6.9%
5 88855
 
6.8%
6 86587
 
6.6%
9 82968
 
6.3%
7 82780
 
6.3%
8 82526
 
6.3%
Other values (11) 2999
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1313567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 284315
21.6%
0 204676
15.6%
2 187630
14.3%
3 118323
9.0%
4 90263
 
6.9%
5 88855
 
6.8%
6 86587
 
6.6%
9 82968
 
6.3%
7 82780
 
6.3%
8 82526
 
6.3%
Other values (61) 4644
 
0.4%

rpt_latitude
Real number (ℝ)

MISSING 

Distinct7976
Distinct (%)77.5%
Missing137456
Missing (%)93.0%
Infinite0
Infinite (%)0.0%
Mean30.297108
Minimum25.83746
Maximum36.50048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:26.362334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum25.83746
5-th percentile30.167333
Q130.236915
median30.29528
Q330.375007
95-th percentile30.44934
Maximum36.50048
Range10.66302
Interquartile range (IQR)0.1380925

Descriptive statistics

Standard deviation0.37692949
Coefficient of variation (CV)0.012441105
Kurtosis149.25706
Mean30.297108
Median Absolute Deviation (MAD)0.06763
Skewness-1.5802874
Sum311878.43
Variance0.14207584
MonotonicityNot monotonic
2024-03-28T16:15:26.445265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.83746 21
 
< 0.1%
30 20
 
< 0.1%
30.4 17
 
< 0.1%
30.39 16
 
< 0.1%
30.38 15
 
< 0.1%
30.41426 13
 
< 0.1%
30.36 13
 
< 0.1%
30.33426 12
 
< 0.1%
30.25943 12
 
< 0.1%
30.41428 12
 
< 0.1%
Other values (7966) 10143
 
6.9%
(Missing) 137456
93.0%
ValueCountFrequency (%)
25.83746 21
< 0.1%
25.83747 1
 
< 0.1%
25.84 1
 
< 0.1%
25.84345 1
 
< 0.1%
25.84563 1
 
< 0.1%
25.85 1
 
< 0.1%
25.855 1
 
< 0.1%
25.9 6
 
< 0.1%
26 7
 
< 0.1%
26.1 1
 
< 0.1%
ValueCountFrequency (%)
36.50048 3
< 0.1%
36.21798 1
 
< 0.1%
36.08 1
 
< 0.1%
36 5
< 0.1%
35.50078 1
 
< 0.1%
35.3508 1
 
< 0.1%
35.1 1
 
< 0.1%
35 1
 
< 0.1%
34 3
< 0.1%
33.505 1
 
< 0.1%

rpt_longitude
Real number (ℝ)

MISSING 

Distinct7264
Distinct (%)70.6%
Missing137456
Missing (%)93.0%
Infinite0
Infinite (%)0.0%
Mean-97.747226
Minimum-106.64592
Maximum-93.50795
Zeros0
Zeros (%)0.0%
Negative10294
Negative (%)7.0%
Memory size1.1 MiB
2024-03-28T16:15:26.527075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-106.64592
5-th percentile-97.8331
Q1-97.75939
median-97.73109
Q3-97.695077
95-th percentile-97.632227
Maximum-93.50795
Range13.13797
Interquartile range (IQR)0.0643125

Descriptive statistics

Standard deviation0.53736526
Coefficient of variation (CV)-0.0054974988
Kurtosis220.56254
Mean-97.747226
Median Absolute Deviation (MAD)0.034205
Skewness-12.603497
Sum-1006209.9
Variance0.28876142
MonotonicityNot monotonic
2024-03-28T16:15:26.610136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-97.7 29
 
< 0.1%
-97 19
 
< 0.1%
-106.64592 18
 
< 0.1%
-97.67 18
 
< 0.1%
-97.79 18
 
< 0.1%
-97.69 15
 
< 0.1%
-97.71 14
 
< 0.1%
-97.79304 14
 
< 0.1%
-97.73 12
 
< 0.1%
-97.69621 12
 
< 0.1%
Other values (7254) 10125
 
6.9%
(Missing) 137456
93.0%
ValueCountFrequency (%)
-106.64592 18
< 0.1%
-106.6459 3
 
< 0.1%
-106.64 1
 
< 0.1%
-106.5 3
 
< 0.1%
-106.45678 1
 
< 0.1%
-106 3
 
< 0.1%
-105.1 1
 
< 0.1%
-105 1
 
< 0.1%
-104.6 1
 
< 0.1%
-104 1
 
< 0.1%
ValueCountFrequency (%)
-93.50795 6
< 0.1%
-93.6 2
 
< 0.1%
-93.7 2
 
< 0.1%
-93.7377 1
 
< 0.1%
-93.73884 1
 
< 0.1%
-93.775 1
 
< 0.1%
-93.786 1
 
< 0.1%
-94 6
< 0.1%
-94.72418 1
 
< 0.1%
-95 2
 
< 0.1%

rpt_block_num
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19611
Missing (%)13.3%
Memory size1.1 MiB

rpt_street_pfx
Categorical

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing67805
Missing (%)45.9%
Memory size1.1 MiB
N
26382 
E
19702 
S
19430 
W
14269 
SW
 
49
Other values (3)
 
113

Length

Max length2
Median length1
Mean length1.0020264
Min length1

Characters and Unicode

Total characters80107
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowS
3rd rowE
4th rowN
5th rowS

Common Values

ValueCountFrequency (%)
N 26382
 
17.9%
E 19702
 
13.3%
S 19430
 
13.2%
W 14269
 
9.7%
SW 49
 
< 0.1%
NE 45
 
< 0.1%
SE 38
 
< 0.1%
NW 30
 
< 0.1%
(Missing) 67805
45.9%

Length

2024-03-28T16:15:26.718207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:26.831914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
n 26382
33.0%
e 19702
24.6%
s 19430
24.3%
w 14269
17.8%
sw 49
 
0.1%
ne 45
 
0.1%
se 38
 
< 0.1%
nw 30
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 26457
33.0%
E 19785
24.7%
S 19517
24.4%
W 14348
17.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 80107
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 26457
33.0%
E 19785
24.7%
S 19517
24.4%
W 14348
17.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 80107
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 26457
33.0%
E 19785
24.7%
S 19517
24.4%
W 14348
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80107
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 26457
33.0%
E 19785
24.7%
S 19517
24.4%
W 14348
17.9%
Distinct9794
Distinct (%)6.6%
Missing3
Missing (%)< 0.1%
Memory size1.1 MiB
2024-03-28T16:15:27.114084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length49
Median length41
Mean length10.456382
Min length1

Characters and Unicode

Total characters1544899
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5596 ?
Unique (%)3.8%

Sample

1st row3707 MANCHACA
2nd rowPALM WAY TO MOPAC NB RAMP
3rd rowBALCONES CLUB DR
4th rowE US 290 HWY SVRD EB
5th rowBEN WHITE
ValueCountFrequency (%)
35 18801
 
5.5%
ih 18084
 
5.3%
nb 12693
 
3.7%
sb 12675
 
3.7%
svrd 11976
 
3.5%
n 11393
 
3.3%
not 10178
 
3.0%
reported 10178
 
3.0%
e 8672
 
2.5%
mopac 8430
 
2.5%
Other values (4401) 218139
63.9%
2024-03-28T16:15:27.566203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
193472
 
12.5%
E 127764
 
8.3%
R 125088
 
8.1%
A 98571
 
6.4%
N 95829
 
6.2%
S 95159
 
6.2%
T 76211
 
4.9%
O 74088
 
4.8%
D 64671
 
4.2%
L 63313
 
4.1%
Other values (27) 530733
34.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1260435
81.6%
Space Separator 193472
 
12.5%
Decimal Number 90992
 
5.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 127764
 
10.1%
R 125088
 
9.9%
A 98571
 
7.8%
N 95829
 
7.6%
S 95159
 
7.5%
T 76211
 
6.0%
O 74088
 
5.9%
D 64671
 
5.1%
L 63313
 
5.0%
I 60884
 
4.8%
Other values (16) 378857
30.1%
Decimal Number
ValueCountFrequency (%)
3 24936
27.4%
5 23342
25.7%
2 10109
11.1%
1 9937
 
10.9%
0 6032
 
6.6%
8 4164
 
4.6%
9 4120
 
4.5%
6 3504
 
3.9%
7 3108
 
3.4%
4 1740
 
1.9%
Space Separator
ValueCountFrequency (%)
193472
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1260435
81.6%
Common 284464
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 127764
 
10.1%
R 125088
 
9.9%
A 98571
 
7.8%
N 95829
 
7.6%
S 95159
 
7.5%
T 76211
 
6.0%
O 74088
 
5.9%
D 64671
 
5.1%
L 63313
 
5.0%
I 60884
 
4.8%
Other values (16) 378857
30.1%
Common
ValueCountFrequency (%)
193472
68.0%
3 24936
 
8.8%
5 23342
 
8.2%
2 10109
 
3.6%
1 9937
 
3.5%
0 6032
 
2.1%
8 4164
 
1.5%
9 4120
 
1.4%
6 3504
 
1.2%
7 3108
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1544899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
193472
 
12.5%
E 127764
 
8.3%
R 125088
 
8.1%
A 98571
 
6.4%
N 95829
 
6.2%
S 95159
 
6.2%
T 76211
 
4.9%
O 74088
 
4.8%
D 64671
 
4.2%
L 63313
 
4.1%
Other values (27) 530733
34.4%

rpt_street_sfx
Categorical

MISSING 

Distinct18
Distinct (%)< 0.1%
Missing50340
Missing (%)34.1%
Memory size1.1 MiB
BLVD
17719 
RD
15192 
ST
14489 
LN
13962 
HWY
12308 
Other values (13)
23740 

Length

Max length4
Median length2
Mean length2.6916538
Min length2

Characters and Unicode

Total characters262194
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRD
2nd rowDR
3rd rowBLVD
4th rowRD
5th rowLN

Common Values

ValueCountFrequency (%)
BLVD 17719
 
12.0%
RD 15192
 
10.3%
ST 14489
 
9.8%
LN 13962
 
9.4%
HWY 12308
 
8.3%
DR 11069
 
7.5%
EXPY 5193
 
3.5%
AVE 3825
 
2.6%
PKWY 1615
 
1.1%
TRL 511
 
0.3%
Other values (8) 1527
 
1.0%
(Missing) 50340
34.1%

Length

2024-03-28T16:15:27.703243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blvd 17719
18.2%
rd 15192
15.6%
st 14489
14.9%
ln 13962
14.3%
hwy 12308
12.6%
dr 11069
11.4%
expy 5193
 
5.3%
ave 3825
 
3.9%
pkwy 1615
 
1.7%
trl 511
 
0.5%
Other values (8) 1527
 
1.6%

Most occurring characters

ValueCountFrequency (%)
D 43980
16.8%
L 32690
12.5%
R 26994
10.3%
V 21614
8.2%
Y 19788
7.5%
B 17719
6.8%
T 15065
 
5.7%
W 14595
 
5.6%
S 14489
 
5.5%
N 13962
 
5.3%
Other values (10) 41298
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 262194
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 43980
16.8%
L 32690
12.5%
R 26994
10.3%
V 21614
8.2%
Y 19788
7.5%
B 17719
6.8%
T 15065
 
5.7%
W 14595
 
5.6%
S 14489
 
5.5%
N 13962
 
5.3%
Other values (10) 41298
15.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 262194
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 43980
16.8%
L 32690
12.5%
R 26994
10.3%
V 21614
8.2%
Y 19788
7.5%
B 17719
6.8%
T 15065
 
5.7%
W 14595
 
5.6%
S 14489
 
5.5%
N 13962
 
5.3%
Other values (10) 41298
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 43980
16.8%
L 32690
12.5%
R 26994
10.3%
V 21614
8.2%
Y 19788
7.5%
B 17719
6.8%
T 15065
 
5.7%
W 14595
 
5.6%
S 14489
 
5.5%
N 13962
 
5.3%
Other values (10) 41298
15.8%

crash_speed_limit
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean34.757526
Minimum-1
Maximum85
Zeros8
Zeros (%)< 0.1%
Negative36104
Negative (%)24.4%
Memory size1.1 MiB
2024-03-28T16:15:27.785847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q120
median40
Q355
95-th percentile65
Maximum85
Range86
Interquartile range (IQR)35

Descriptive statistics

Standard deviation23.232703
Coefficient of variation (CV)0.66842222
Kurtosis-1.0117395
Mean34.757526
Median Absolute Deviation (MAD)15
Skewness-0.41792648
Sum5135355
Variance539.75848
MonotonicityNot monotonic
2024-03-28T16:15:27.900239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
-1 36104
24.4%
35 21947
14.9%
45 19101
12.9%
55 14227
 
9.6%
30 12004
 
8.1%
65 10764
 
7.3%
40 9296
 
6.3%
50 7569
 
5.1%
60 6986
 
4.7%
70 5408
 
3.7%
Other values (18) 4342
 
2.9%
ValueCountFrequency (%)
-1 36104
24.4%
0 8
 
< 0.1%
5 67
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
10 190
 
0.1%
15 392
 
0.3%
20 320
 
0.2%
24 2
 
< 0.1%
25 2270
 
1.5%
ValueCountFrequency (%)
85 6
 
< 0.1%
80 176
 
0.1%
79 1
 
< 0.1%
75 893
 
0.6%
70 5408
 
3.7%
66 1
 
< 0.1%
65 10764
7.3%
60 6986
4.7%
55 14227
9.6%
50 7569
5.1%

road_constr_zone_fl
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size288.7 KiB
False
139901 
True
 
7847
(Missing)
 
2
ValueCountFrequency (%)
False 139901
94.7%
True 7847
 
5.3%
(Missing) 2
 
< 0.1%
2024-03-28T16:15:27.965741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

latitude
Real number (ℝ)

MISSING 

Distinct96355
Distinct (%)66.2%
Missing2243
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean30.298798
Minimum30.098737
Maximum30.511625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:28.060017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum30.098737
5-th percentile30.177655
Q130.233272
median30.284661
Q330.36443
95-th percentile30.442425
Maximum30.511625
Range0.41288743
Interquartile range (IQR)0.13115791

Descriptive statistics

Standard deviation0.083391975
Coefficient of variation (CV)0.0027523195
Kurtosis-0.80132875
Mean30.298798
Median Absolute Deviation (MAD)0.06247375
Skewness0.28570097
Sum4408687.2
Variance0.0069542214
MonotonicityNot monotonic
2024-03-28T16:15:28.154997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.24915123 133
 
0.1%
30.33917999 112
 
0.1%
30.20236015 109
 
0.1%
30.27753478 109
 
0.1%
30.32556534 108
 
0.1%
30.33882963 95
 
0.1%
30.34774163 91
 
0.1%
30.44802363 90
 
0.1%
30.23557091 88
 
0.1%
30.38954926 83
 
0.1%
Other values (96345) 144489
97.8%
(Missing) 2243
 
1.5%
ValueCountFrequency (%)
30.0987373 1
< 0.1%
30.09889195 1
< 0.1%
30.0989809 1
< 0.1%
30.09899192 1
< 0.1%
30.09902058 1
< 0.1%
30.09906538 1
< 0.1%
30.09907731 1
< 0.1%
30.09908481 1
< 0.1%
30.09911847 1
< 0.1%
30.09919882 1
< 0.1%
ValueCountFrequency (%)
30.51162473 1
< 0.1%
30.50985091 1
< 0.1%
30.50981473 1
< 0.1%
30.50938474 1
< 0.1%
30.50938473 1
< 0.1%
30.50775084 1
< 0.1%
30.50774627 1
< 0.1%
30.50707863 1
< 0.1%
30.50625252 1
< 0.1%
30.50616954 1
< 0.1%

longitude
Real number (ℝ)

MISSING 

Distinct96230
Distinct (%)66.1%
Missing2243
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean-97.737771
Minimum-97.926789
Maximum-97.570148
Zeros0
Zeros (%)0.0%
Negative145507
Negative (%)98.5%
Memory size1.1 MiB
2024-03-28T16:15:28.447332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-97.926789
5-th percentile-97.83319
Q1-97.769578
median-97.736145
Q3-97.701296
95-th percentile-97.659489
Maximum-97.570148
Range0.35664108
Interquartile range (IQR)0.06828216

Descriptive statistics

Standard deviation0.052714147
Coefficient of variation (CV)-0.00053934263
Kurtosis0.22937423
Mean-97.737771
Median Absolute Deviation (MAD)0.034089563
Skewness-0.26888792
Sum-14221530
Variance0.0027787813
MonotonicityNot monotonic
2024-03-28T16:15:28.541457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-97.80529785 133
 
0.1%
-97.70010376 120
 
0.1%
-97.63785553 109
 
0.1%
-97.67331696 109
 
0.1%
-97.74195246 108
 
0.1%
-97.69958145 95
 
0.1%
-97.71247936 91
 
0.1%
-97.79304577 90
 
0.1%
-97.82463074 88
 
0.1%
-97.74504852 83
 
0.1%
Other values (96220) 144481
97.8%
(Missing) 2243
 
1.5%
ValueCountFrequency (%)
-97.92678889 1
< 0.1%
-97.92658994 1
< 0.1%
-97.92493512 1
< 0.1%
-97.92453454 1
< 0.1%
-97.92448327 1
< 0.1%
-97.92442406 1
< 0.1%
-97.92431681 1
< 0.1%
-97.924013 1
< 0.1%
-97.92313723 1
< 0.1%
-97.92259045 1
< 0.1%
ValueCountFrequency (%)
-97.57014781 1
< 0.1%
-97.57024774 1
< 0.1%
-97.5702853 1
< 0.1%
-97.57029177 1
< 0.1%
-97.57040391 1
< 0.1%
-97.57054233 1
< 0.1%
-97.57074071 1
< 0.1%
-97.57075081 1
< 0.1%
-97.57102709 1
< 0.1%
-97.57103645 1
< 0.1%
Distinct4630
Distinct (%)3.1%
Missing2
Missing (%)< 0.1%
Memory size1.1 MiB
2024-03-28T16:15:28.845440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length9.2789682
Min length3

Characters and Unicode

Total characters1370949
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2208 ?
Unique (%)1.5%

Sample

1st row3707 MANCHACA RD
2nd rowPALM WAY TO MOPAC NB RAMP
3rd rowBALCONES CLUB DR
4th rowUS0290
5th rowUS0290
ValueCountFrequency (%)
ih0035 24841
 
8.9%
st 16185
 
5.8%
e 12954
 
4.6%
rd 12785
 
4.6%
dr 12668
 
4.5%
us0183 12105
 
4.3%
ln 11109
 
4.0%
w 10828
 
3.9%
blvd 10220
 
3.7%
sl0001 9023
 
3.2%
Other values (3316) 146218
52.4%
2024-03-28T16:15:29.271254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
131188
 
9.6%
0 129302
 
9.4%
S 103032
 
7.5%
R 85099
 
6.2%
L 77592
 
5.7%
E 77389
 
5.6%
A 61412
 
4.5%
N 58169
 
4.2%
I 57721
 
4.2%
D 55131
 
4.0%
Other values (27) 534914
39.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 924884
67.5%
Decimal Number 314877
 
23.0%
Space Separator 131188
 
9.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 103032
11.1%
R 85099
 
9.2%
L 77592
 
8.4%
E 77389
 
8.4%
A 61412
 
6.6%
N 58169
 
6.3%
I 57721
 
6.2%
D 55131
 
6.0%
T 53651
 
5.8%
H 51166
 
5.5%
Other values (16) 244522
26.4%
Decimal Number
ValueCountFrequency (%)
0 129302
41.1%
3 52379
16.6%
1 33623
 
10.7%
5 33186
 
10.5%
2 19676
 
6.2%
8 13680
 
4.3%
7 10392
 
3.3%
9 8173
 
2.6%
4 7692
 
2.4%
6 6774
 
2.2%
Space Separator
ValueCountFrequency (%)
131188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 924884
67.5%
Common 446065
32.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 103032
11.1%
R 85099
 
9.2%
L 77592
 
8.4%
E 77389
 
8.4%
A 61412
 
6.6%
N 58169
 
6.3%
I 57721
 
6.2%
D 55131
 
6.0%
T 53651
 
5.8%
H 51166
 
5.5%
Other values (16) 244522
26.4%
Common
ValueCountFrequency (%)
131188
29.4%
0 129302
29.0%
3 52379
 
11.7%
1 33623
 
7.5%
5 33186
 
7.4%
2 19676
 
4.4%
8 13680
 
3.1%
7 10392
 
2.3%
9 8173
 
1.8%
4 7692
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1370949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
131188
 
9.6%
0 129302
 
9.4%
S 103032
 
7.5%
R 85099
 
6.2%
L 77592
 
5.7%
E 77389
 
5.6%
A 61412
 
4.5%
N 58169
 
4.2%
I 57721
 
4.2%
D 55131
 
4.0%
Other values (27) 534914
39.0%

street_nbr
Real number (ℝ)

MISSING 

Distinct9826
Distinct (%)16.2%
Missing87038
Missing (%)58.9%
Infinite0
Infinite (%)0.0%
Mean4504.3615
Minimum0
Maximum21146
Zeros46
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:29.397968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300
Q11300
median3298
Q37199
95-th percentile11700
Maximum21146
Range21146
Interquartile range (IQR)5899

Descriptive statistics

Standard deviation3757.2097
Coefficient of variation (CV)0.83412704
Kurtosis-0.58026084
Mean4504.3615
Median Absolute Deviation (MAD)2402
Skewness0.74779783
Sum2.734688 × 108
Variance14116625
MonotonicityNot monotonic
2024-03-28T16:15:29.524313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 582
 
0.4%
600 421
 
0.3%
800 403
 
0.3%
400 395
 
0.3%
1000 393
 
0.3%
900 392
 
0.3%
500 355
 
0.2%
1100 325
 
0.2%
1900 312
 
0.2%
300 299
 
0.2%
Other values (9816) 56835
38.5%
(Missing) 87038
58.9%
ValueCountFrequency (%)
0 46
< 0.1%
1 5
 
< 0.1%
2 1
 
< 0.1%
4 20
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
21146 1
 
< 0.1%
20032 1
 
< 0.1%
16799 1
 
< 0.1%
16600 1
 
< 0.1%
16501 1
 
< 0.1%
16400 1
 
< 0.1%
16300 2
< 0.1%
16200 1
 
< 0.1%
16157 3
< 0.1%
16126 1
 
< 0.1%

street_name_2
Text

MISSING 

Distinct3396
Distinct (%)5.1%
Missing81474
Missing (%)55.1%
Memory size1.1 MiB
2024-03-28T16:15:29.851058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length10.161929
Min length3

Characters and Unicode

Total characters673492
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1410 ?
Unique (%)2.1%

Sample

1st rowUNKNOWN
2nd rowVICTOR ST
3rd rowSH0071
4th rowS 1ST ST
5th rowCOLLINFIELD DR
ValueCountFrequency (%)
st 10325
 
7.7%
unknown 9867
 
7.3%
dr 8316
 
6.2%
ln 5983
 
4.4%
rd 5709
 
4.2%
e 5656
 
4.2%
w 4926
 
3.7%
blvd 4276
 
3.2%
not 2632
 
2.0%
reported 2632
 
2.0%
Other values (2635) 74436
55.2%
2024-03-28T16:15:30.428312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
68482
 
10.2%
N 62626
 
9.3%
R 50876
 
7.6%
E 45809
 
6.8%
S 39042
 
5.8%
O 37239
 
5.5%
L 35991
 
5.3%
T 35302
 
5.2%
A 33381
 
5.0%
D 33314
 
4.9%
Other values (27) 231430
34.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 543498
80.7%
Space Separator 68482
 
10.2%
Decimal Number 61512
 
9.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 62626
11.5%
R 50876
 
9.4%
E 45809
 
8.4%
S 39042
 
7.2%
O 37239
 
6.9%
L 35991
 
6.6%
T 35302
 
6.5%
A 33381
 
6.1%
D 33314
 
6.1%
W 22565
 
4.2%
Other values (16) 147353
27.1%
Decimal Number
ValueCountFrequency (%)
0 19866
32.3%
3 9010
14.6%
1 6940
 
11.3%
2 5829
 
9.5%
5 5020
 
8.2%
4 4274
 
6.9%
7 3263
 
5.3%
9 2733
 
4.4%
8 2345
 
3.8%
6 2232
 
3.6%
Space Separator
ValueCountFrequency (%)
68482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 543498
80.7%
Common 129994
 
19.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 62626
11.5%
R 50876
 
9.4%
E 45809
 
8.4%
S 39042
 
7.2%
O 37239
 
6.9%
L 35991
 
6.6%
T 35302
 
6.5%
A 33381
 
6.1%
D 33314
 
6.1%
W 22565
 
4.2%
Other values (16) 147353
27.1%
Common
ValueCountFrequency (%)
68482
52.7%
0 19866
 
15.3%
3 9010
 
6.9%
1 6940
 
5.3%
2 5829
 
4.5%
5 5020
 
3.9%
4 4274
 
3.3%
7 3263
 
2.5%
9 2733
 
2.1%
8 2345
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 673492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
68482
 
10.2%
N 62626
 
9.3%
R 50876
 
7.6%
E 45809
 
6.8%
S 39042
 
5.8%
O 37239
 
5.5%
L 35991
 
5.3%
T 35302
 
5.2%
A 33381
 
5.0%
D 33314
 
4.9%
Other values (27) 231430
34.4%

street_nbr_2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing147750
Missing (%)100.0%
Memory size1.1 MiB

crash_sev_id
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7065245
Minimum0
Maximum99
Zeros4851
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:30.637048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q35
95-th percentile5
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7536941
Coefficient of variation (CV)0.47313705
Kurtosis812.86388
Mean3.7065245
Median Absolute Deviation (MAD)0
Skewness14.637743
Sum547639
Variance3.0754429
MonotonicityNot monotonic
2024-03-28T16:15:30.718660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 77274
52.3%
3 31307
21.2%
2 29125
 
19.7%
0 4851
 
3.3%
1 4333
 
2.9%
4 846
 
0.6%
99 13
 
< 0.1%
94 1
 
< 0.1%
ValueCountFrequency (%)
0 4851
 
3.3%
1 4333
 
2.9%
2 29125
 
19.7%
3 31307
21.2%
4 846
 
0.6%
5 77274
52.3%
94 1
 
< 0.1%
99 13
 
< 0.1%
ValueCountFrequency (%)
99 13
 
< 0.1%
94 1
 
< 0.1%
5 77274
52.3%
4 846
 
0.6%
3 31307
21.2%
2 29125
 
19.7%
1 4333
 
2.9%
0 4851
 
3.3%

sus_serious_injry_cnt
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03386802
Minimum0
Maximum10
Zeros143326
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:30.795679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2074838
Coefficient of variation (CV)6.1262452
Kurtosis112.12177
Mean0.03386802
Median Absolute Deviation (MAD)0
Skewness8.1716329
Sum5004
Variance0.043049526
MonotonicityNot monotonic
2024-03-28T16:15:30.892837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 143326
97.0%
1 3964
 
2.7%
2 375
 
0.3%
3 63
 
< 0.1%
4 14
 
< 0.1%
5 7
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 143326
97.0%
1 3964
 
2.7%
2 375
 
0.3%
3 63
 
< 0.1%
4 14
 
< 0.1%
5 7
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
5 7
 
< 0.1%
4 14
 
< 0.1%
3 63
 
< 0.1%
2 375
 
0.3%
1 3964
 
2.7%
0 143326
97.0%

nonincap_injry_cnt
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.26892906
Minimum0
Maximum14
Zeros117724
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:30.989625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62503417
Coefficient of variation (CV)2.32416
Kurtosis19.779645
Mean0.26892906
Median Absolute Deviation (MAD)0
Skewness3.4185558
Sum39734
Variance0.39066771
MonotonicityNot monotonic
2024-03-28T16:15:31.107350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 117724
79.7%
1 23211
 
15.7%
2 4919
 
3.3%
3 1274
 
0.9%
4 408
 
0.3%
5 134
 
0.1%
6 38
 
< 0.1%
7 22
 
< 0.1%
9 8
 
< 0.1%
8 5
 
< 0.1%
Other values (4) 6
 
< 0.1%
ValueCountFrequency (%)
0 117724
79.7%
1 23211
 
15.7%
2 4919
 
3.3%
3 1274
 
0.9%
4 408
 
0.3%
5 134
 
0.1%
6 38
 
< 0.1%
7 22
 
< 0.1%
8 5
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 8
 
< 0.1%
8 5
 
< 0.1%
7 22
 
< 0.1%
6 38
 
< 0.1%
5 134
 
0.1%
4 408
0.3%

poss_injry_cnt
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.33921042
Minimum0
Maximum20
Zeros111987
Zeros (%)75.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:31.236095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72828525
Coefficient of variation (CV)2.1470014
Kurtosis22.727939
Mean0.33921042
Median Absolute Deviation (MAD)0
Skewness3.4178821
Sum50118
Variance0.53039941
MonotonicityNot monotonic
2024-03-28T16:15:31.373068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 111987
75.8%
1 26191
 
17.7%
2 6626
 
4.5%
3 1900
 
1.3%
4 611
 
0.4%
5 250
 
0.2%
6 106
 
0.1%
7 38
 
< 0.1%
8 20
 
< 0.1%
9 9
 
< 0.1%
Other values (6) 11
 
< 0.1%
ValueCountFrequency (%)
0 111987
75.8%
1 26191
 
17.7%
2 6626
 
4.5%
3 1900
 
1.3%
4 611
 
0.4%
5 250
 
0.2%
6 106
 
0.1%
7 38
 
< 0.1%
8 20
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 1
 
< 0.1%
15 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 5
 
< 0.1%
9 9
 
< 0.1%
8 20
 
< 0.1%
7 38
 
< 0.1%
6 106
0.1%

non_injry_cnt
Real number (ℝ)

ZEROS 

Distinct46
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.850246
Minimum0
Maximum56
Zeros24343
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:31.528478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile5
Maximum56
Range56
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6371448
Coefficient of variation (CV)0.88482545
Kurtosis81.749938
Mean1.850246
Median Absolute Deviation (MAD)1
Skewness4.5994471
Sum273372
Variance2.680243
MonotonicityNot monotonic
2024-03-28T16:15:31.684275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2 44400
30.1%
1 43213
29.2%
0 24343
16.5%
3 19249
13.0%
4 8576
 
5.8%
5 4199
 
2.8%
6 1853
 
1.3%
7 875
 
0.6%
8 467
 
0.3%
9 225
 
0.2%
Other values (36) 349
 
0.2%
ValueCountFrequency (%)
0 24343
16.5%
1 43213
29.2%
2 44400
30.1%
3 19249
13.0%
4 8576
 
5.8%
5 4199
 
2.8%
6 1853
 
1.3%
7 875
 
0.6%
8 467
 
0.3%
9 225
 
0.2%
ValueCountFrequency (%)
56 1
 
< 0.1%
53 1
 
< 0.1%
50 1
 
< 0.1%
48 2
< 0.1%
46 2
< 0.1%
44 1
 
< 0.1%
43 3
< 0.1%
42 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%

unkn_injry_cnt
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.11606925
Minimum0
Maximum41
Zeros133316
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:31.818978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41838505
Coefficient of variation (CV)3.6046157
Kurtosis714.07749
Mean0.11606925
Median Absolute Deviation (MAD)0
Skewness12.26397
Sum17149
Variance0.17504605
MonotonicityNot monotonic
2024-03-28T16:15:31.912918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 133316
90.2%
1 12660
 
8.6%
2 1242
 
0.8%
3 342
 
0.2%
4 99
 
0.1%
5 55
 
< 0.1%
6 14
 
< 0.1%
7 8
 
< 0.1%
8 4
 
< 0.1%
14 2
 
< 0.1%
Other values (6) 6
 
< 0.1%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
0 133316
90.2%
1 12660
 
8.6%
2 1242
 
0.8%
3 342
 
0.2%
4 99
 
0.1%
5 55
 
< 0.1%
6 14
 
< 0.1%
7 8
 
< 0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 4
 
< 0.1%
7 8
< 0.1%
6 14
< 0.1%

tot_injry_cnt
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.64200531
Minimum0
Maximum21
Zeros82717
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:31.996575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93467226
Coefficient of variation (CV)1.4558638
Kurtosis12.030265
Mean0.64200531
Median Absolute Deviation (MAD)0
Skewness2.4049326
Sum94855
Variance0.87361223
MonotonicityNot monotonic
2024-03-28T16:15:32.077679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 82717
56.0%
1 45513
30.8%
2 13202
 
8.9%
3 4030
 
2.7%
4 1366
 
0.9%
5 526
 
0.4%
6 213
 
0.1%
7 89
 
0.1%
8 49
 
< 0.1%
9 18
 
< 0.1%
Other values (8) 25
 
< 0.1%
ValueCountFrequency (%)
0 82717
56.0%
1 45513
30.8%
2 13202
 
8.9%
3 4030
 
2.7%
4 1366
 
0.9%
5 526
 
0.4%
6 213
 
0.1%
7 89
 
0.1%
8 49
 
< 0.1%
9 18
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
18 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 6
 
< 0.1%
11 3
 
< 0.1%
10 10
 
< 0.1%
9 18
 
< 0.1%
8 49
< 0.1%

death_cnt
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
146905 
1
 
808
2
 
32
3
 
3
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 146905
99.4%
1 808
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Length

2024-03-28T16:15:32.189086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:32.286867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 146905
99.4%
1 808
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 146905
99.4%
1 808
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 146905
99.4%
1 808
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 146905
99.4%
1 808
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 146905
99.4%
1 808
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

contrib_factr_p1_id
Real number (ℝ)

MISSING 

Distinct70
Distinct (%)0.2%
Missing119143
Missing (%)80.6%
Infinite0
Infinite (%)0.0%
Mean33.357535
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:32.394078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q120
median20
Q345
95-th percentile74
Maximum80
Range79
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.899472
Coefficient of variation (CV)0.59655104
Kurtosis-0.64439081
Mean33.357535
Median Absolute Deviation (MAD)2
Skewness0.82479442
Sum954259
Variance395.98899
MonotonicityNot monotonic
2024-03-28T16:15:32.539762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 10714
 
7.3%
22 1972
 
1.3%
19 1795
 
1.2%
74 1546
 
1.0%
60 1458
 
1.0%
44 1424
 
1.0%
67 1282
 
0.9%
45 974
 
0.7%
4 907
 
0.6%
41 734
 
0.5%
Other values (60) 5801
 
3.9%
(Missing) 119143
80.6%
ValueCountFrequency (%)
1 58
 
< 0.1%
2 44
 
< 0.1%
3 54
 
< 0.1%
4 907
0.6%
14 47
 
< 0.1%
15 547
 
0.4%
16 461
 
0.3%
17 54
 
< 0.1%
18 7
 
< 0.1%
19 1795
1.2%
ValueCountFrequency (%)
80 1
 
< 0.1%
79 4
 
< 0.1%
78 89
 
0.1%
77 76
 
0.1%
76 52
 
< 0.1%
75 34
 
< 0.1%
74 1546
1.0%
73 122
 
0.1%
72 17
 
< 0.1%
71 14
 
< 0.1%

contrib_factr_p2_id
Real number (ℝ)

MISSING 

Distinct65
Distinct (%)1.4%
Missing143235
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean36.347287
Minimum1
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:32.646833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q120
median22
Q360
95-th percentile74
Maximum79
Range78
Interquartile range (IQR)40

Descriptive statistics

Standard deviation20.583402
Coefficient of variation (CV)0.56629817
Kurtosis-1.0834528
Mean36.347287
Median Absolute Deviation (MAD)7
Skewness0.56565121
Sum164108
Variance423.67644
MonotonicityNot monotonic
2024-03-28T16:15:32.770264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 1137
 
0.8%
19 484
 
0.3%
22 418
 
0.3%
60 277
 
0.2%
44 253
 
0.2%
67 236
 
0.2%
68 206
 
0.1%
74 196
 
0.1%
45 194
 
0.1%
41 156
 
0.1%
Other values (55) 958
 
0.6%
(Missing) 143235
96.9%
ValueCountFrequency (%)
1 17
 
< 0.1%
2 17
 
< 0.1%
3 5
 
< 0.1%
4 94
 
0.1%
14 2
 
< 0.1%
15 46
 
< 0.1%
16 46
 
< 0.1%
17 7
 
< 0.1%
18 2
 
< 0.1%
19 484
0.3%
ValueCountFrequency (%)
79 1
 
< 0.1%
78 28
 
< 0.1%
77 19
 
< 0.1%
76 13
 
< 0.1%
75 18
 
< 0.1%
74 196
0.1%
73 19
 
< 0.1%
72 3
 
< 0.1%
71 5
 
< 0.1%
69 1
 
< 0.1%
Distinct1112
Distinct (%)0.8%
Missing7
Missing (%)< 0.1%
Memory size1.1 MiB
2024-03-28T16:15:33.124095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length321
Median length307
Mean length37.533656
Min length10

Characters and Unicode

Total characters5545335
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique593 ?
Unique (%)0.4%

Sample

1st rowPassenger car
2nd rowLarge passenger vehicle
3rd rowLarge passenger vehicle
4th rowMotor vehicle – other
5th rowPassenger car
ValueCountFrequency (%)
passenger 278045
31.6%
car 172791
19.6%
157282
17.9%
vehicle 116957
13.3%
large 105254
 
12.0%
motor 11703
 
1.3%
– 11703
 
1.3%
other 11703
 
1.3%
other/unknown 5061
 
0.6%
pedestrian 3705
 
0.4%
Other values (6) 6556
 
0.7%
2024-03-28T16:15:33.567226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 925973
16.7%
733017
13.2%
r 592303
10.7%
s 560112
10.1%
a 559810
10.1%
g 383299
6.9%
c 302423
 
5.5%
n 296948
 
5.4%
P 176496
 
3.2%
& 157282
 
2.8%
Other values (23) 857672
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4327869
78.0%
Space Separator 733017
 
13.2%
Uppercase Letter 310086
 
5.6%
Other Punctuation 162343
 
2.9%
Dash Punctuation 12020
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 925973
21.4%
r 592303
13.7%
s 560112
12.9%
a 559810
12.9%
g 383299
8.9%
c 302423
 
7.0%
n 296948
 
6.9%
h 133721
 
3.1%
i 123327
 
2.8%
l 123136
 
2.8%
Other values (10) 326817
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
P 176496
56.9%
L 105254
33.9%
M 15412
 
5.0%
O 5061
 
1.6%
U 5061
 
1.6%
B 2470
 
0.8%
E 317
 
0.1%
T 15
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
& 157282
96.9%
/ 5061
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
– 11703
97.4%
- 317
 
2.6%
Space Separator
ValueCountFrequency (%)
733017
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4637955
83.6%
Common 907380
 
16.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 925973
20.0%
r 592303
12.8%
s 560112
12.1%
a 559810
12.1%
g 383299
8.3%
c 302423
 
6.5%
n 296948
 
6.4%
P 176496
 
3.8%
h 133721
 
2.9%
i 123327
 
2.7%
Other values (18) 583543
12.6%
Common
ValueCountFrequency (%)
733017
80.8%
& 157282
 
17.3%
– 11703
 
1.3%
/ 5061
 
0.6%
- 317
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5533632
99.8%
Punctuation 11703
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 925973
16.7%
733017
13.2%
r 592303
10.7%
s 560112
10.1%
a 559810
10.1%
g 383299
6.9%
c 302423
 
5.5%
n 296948
 
5.4%
P 176496
 
3.2%
& 157282
 
2.8%
Other values (22) 845969
15.3%
Punctuation
ValueCountFrequency (%)
– 11703
100.0%
Distinct147743
Distinct (%)100.0%
Missing7
Missing (%)< 0.1%
Memory size1.1 MiB
2024-03-28T16:15:34.094166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length3936
Median length3325
Mean length455.91029
Min length214

Characters and Unicode

Total characters67357554
Distinct characters52
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique147743 ?
Unique (%)100.0%

Sample

1st row[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 2259431, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 4, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]
2nd row[{"mode_id": 2, "mode_desc": "Large passenger vehicle", "unit_id": 2260826, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]
3rd row[{"mode_id": 2, "mode_desc": "Large passenger vehicle", "unit_id": 2260877, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]
4th row[{"mode_id": 4, "mode_desc": "Motor vehicle \u2013 other", "unit_id": 2262706, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]
5th row[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 2262540, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]
ValueCountFrequency (%)
0 1750115
26.8%
1 493130
 
7.6%
mode_id 305025
 
4.7%
tot_injry_cnt 305025
 
4.7%
unkn_injry_cnt 305025
 
4.7%
non_injry_cnt 305025
 
4.7%
poss_injry_cnt 305025
 
4.7%
nonincap_injry_cnt 305025
 
4.7%
sus_serious_injry_cnt 305025
 
4.7%
death_cnt 305025
 
4.7%
Other values (305087) 1835508
28.2%
2024-03-28T16:15:35.351113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 6710550
 
10.0%
n 6702473
 
10.0%
6371210
 
9.5%
_ 5185425
 
7.7%
i 3478602
 
5.2%
t 3391473
 
5.0%
: 3050250
 
4.5%
c 3047648
 
4.5%
, 2902507
 
4.3%
r 2727478
 
4.0%
Other values (42) 23789938
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37282272
55.3%
Other Punctuation 12680071
 
18.8%
Space Separator 6371210
 
9.5%
Connector Punctuation 5185425
 
7.7%
Decimal Number 4622637
 
6.9%
Close Punctuation 452768
 
0.7%
Open Punctuation 452768
 
0.7%
Uppercase Letter 310086
 
0.5%
Dash Punctuation 317
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 6702473
18.0%
i 3478602
9.3%
t 3391473
9.1%
c 3047648
8.2%
r 2727478
 
7.3%
s 2695287
 
7.2%
e 2451098
 
6.6%
o 2183397
 
5.9%
y 1836329
 
4.9%
d 1833900
 
4.9%
Other values (12) 6934587
18.6%
Decimal Number
ValueCountFrequency (%)
0 1924318
41.6%
1 658281
 
14.2%
2 532568
 
11.5%
4 288753
 
6.2%
3 239268
 
5.2%
6 221661
 
4.8%
7 209595
 
4.5%
5 204291
 
4.4%
8 173861
 
3.8%
9 170041
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
P 176496
56.9%
L 105254
33.9%
M 15412
 
5.0%
O 5061
 
1.6%
U 5061
 
1.6%
B 2470
 
0.8%
E 317
 
0.1%
T 15
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
" 6710550
52.9%
: 3050250
24.1%
, 2902507
22.9%
\ 11703
 
0.1%
/ 5061
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
} 305025
67.4%
] 147743
32.6%
Open Punctuation
ValueCountFrequency (%)
{ 305025
67.4%
[ 147743
32.6%
Space Separator
ValueCountFrequency (%)
6371210
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5185425
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 317
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37592358
55.8%
Common 29765196
44.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 6702473
17.8%
i 3478602
9.3%
t 3391473
9.0%
c 3047648
8.1%
r 2727478
 
7.3%
s 2695287
 
7.2%
e 2451098
 
6.5%
o 2183397
 
5.8%
y 1836329
 
4.9%
d 1833900
 
4.9%
Other values (20) 7244673
19.3%
Common
ValueCountFrequency (%)
" 6710550
22.5%
6371210
21.4%
_ 5185425
17.4%
: 3050250
10.2%
, 2902507
9.8%
0 1924318
 
6.5%
1 658281
 
2.2%
2 532568
 
1.8%
} 305025
 
1.0%
{ 305025
 
1.0%
Other values (12) 1820037
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67357554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 6710550
 
10.0%
n 6702473
 
10.0%
6371210
 
9.5%
_ 5185425
 
7.7%
i 3478602
 
5.2%
t 3391473
 
5.0%
: 3050250
 
4.5%
c 3047648
 
4.5%
, 2902507
 
4.3%
r 2727478
 
4.0%
Other values (42) 23789938
35.3%

pedestrian_fl
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing144245
Missing (%)97.6%
Memory size288.7 KiB
True
 
3505
(Missing)
144245 
ValueCountFrequency (%)
True 3505
 
2.4%
(Missing) 144245
97.6%
2024-03-28T16:15:35.506292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

motor_vehicle_fl
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1116
Missing (%)0.8%
Memory size288.7 KiB
True
146634 
(Missing)
 
1116
ValueCountFrequency (%)
True 146634
99.2%
(Missing) 1116
 
0.8%
2024-03-28T16:15:35.640399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

motorcycle_fl
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing144148
Missing (%)97.6%
Memory size288.7 KiB
True
 
3602
(Missing)
144148 
ValueCountFrequency (%)
True 3602
 
2.4%
(Missing) 144148
97.6%
2024-03-28T16:15:35.811248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

bicycle_fl
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing145306
Missing (%)98.3%
Memory size288.7 KiB
True
 
2444
(Missing)
145306 
ValueCountFrequency (%)
True 2444
 
1.7%
(Missing) 145306
98.3%
2024-03-28T16:15:35.910956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

other_fl
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing142905
Missing (%)96.7%
Memory size288.7 KiB
True
 
4845
(Missing)
142905 
ValueCountFrequency (%)
True 4845
 
3.3%
(Missing) 142905
96.7%
2024-03-28T16:15:35.982268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

point
Text

MISSING 

Distinct97739
Distinct (%)67.2%
Missing2243
Missing (%)1.5%
Memory size1.1 MiB
2024-03-28T16:15:36.376269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length45
Median length32
Mean length34.597174
Min length22

Characters and Unicode

Total characters5034131
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85923 ?
Unique (%)59.1%

Sample

1st rowPOINT (-97.72445 30.404)
2nd rowPOINT (-97.78855804 30.43798421)
3rd rowPOINT (-97.6626808595204 30.3276207970184)
4th rowPOINT (-97.76719944 30.22515795)
5th rowPOINT (-97.78425218 30.16984339)
ValueCountFrequency (%)
point 145507
33.3%
97.80529785 133
 
< 0.1%
30.24915123 133
 
< 0.1%
97.70010376 120
 
< 0.1%
30.33917999 112
 
< 0.1%
97.63785553 109
 
< 0.1%
30.20236015 109
 
< 0.1%
97.67331696 109
 
< 0.1%
30.27753478 109
 
< 0.1%
30.32556534 108
 
< 0.1%
Other values (192616) 289972
66.4%
2024-03-28T16:15:36.997087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 509037
 
10.1%
3 428304
 
8.5%
9 375986
 
7.5%
0 347207
 
6.9%
2 316857
 
6.3%
4 308324
 
6.1%
. 291014
 
5.8%
291014
 
5.8%
6 277678
 
5.5%
8 253877
 
5.0%
Other values (10) 1634833
32.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3288047
65.3%
Uppercase Letter 727535
 
14.5%
Other Punctuation 291014
 
5.8%
Space Separator 291014
 
5.8%
Dash Punctuation 145507
 
2.9%
Open Punctuation 145507
 
2.9%
Close Punctuation 145507
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 509037
15.5%
3 428304
13.0%
9 375986
11.4%
0 347207
10.6%
2 316857
9.6%
4 308324
9.4%
6 277678
8.4%
8 253877
7.7%
1 237857
7.2%
5 232920
7.1%
Uppercase Letter
ValueCountFrequency (%)
O 145507
20.0%
T 145507
20.0%
N 145507
20.0%
I 145507
20.0%
P 145507
20.0%
Other Punctuation
ValueCountFrequency (%)
. 291014
100.0%
Space Separator
ValueCountFrequency (%)
291014
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 145507
100.0%
Open Punctuation
ValueCountFrequency (%)
( 145507
100.0%
Close Punctuation
ValueCountFrequency (%)
) 145507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4306596
85.5%
Latin 727535
 
14.5%

Most frequent character per script

Common
ValueCountFrequency (%)
7 509037
11.8%
3 428304
9.9%
9 375986
8.7%
0 347207
 
8.1%
2 316857
 
7.4%
4 308324
 
7.2%
. 291014
 
6.8%
291014
 
6.8%
6 277678
 
6.4%
8 253877
 
5.9%
Other values (5) 907298
21.1%
Latin
ValueCountFrequency (%)
O 145507
20.0%
T 145507
20.0%
N 145507
20.0%
I 145507
20.0%
P 145507
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5034131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 509037
 
10.1%
3 428304
 
8.5%
9 375986
 
7.5%
0 347207
 
6.9%
2 316857
 
6.3%
4 308324
 
6.1%
. 291014
 
5.8%
291014
 
5.8%
6 277678
 
5.5%
8 253877
 
5.0%
Other values (10) 1634833
32.5%

apd_confirmed_fatality
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size144.4 KiB
False
146908 
True
 
842
ValueCountFrequency (%)
False 146908
99.4%
True 842
 
0.6%
2024-03-28T16:15:37.168674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

apd_confirmed_death_count
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
146908 
1
 
805
2
 
32
3
 
3
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 146908
99.4%
1 805
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Length

2024-03-28T16:15:37.249232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:37.319843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 146908
99.4%
1 805
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 146908
99.4%
1 805
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 146908
99.4%
1 805
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 146908
99.4%
1 805
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 146908
99.4%
1 805
 
0.5%
2 32
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

motor_vehicle_death_count
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147372 
1
 
345
2
 
28
3
 
3
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147372
99.7%
1 345
 
0.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Length

2024-03-28T16:15:37.435469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:37.526393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147372
99.7%
1 345
 
0.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147372
99.7%
1 345
 
0.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147372
99.7%
1 345
 
0.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147372
99.7%
1 345
 
0.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147372
99.7%
1 345
 
0.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%

motor_vehicle_serious_injury_count
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.022585448
Minimum0
Maximum5
Zeros144928
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-28T16:15:37.598237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17594152
Coefficient of variation (CV)7.790039
Kurtosis134.62088
Mean0.022585448
Median Absolute Deviation (MAD)0
Skewness10.008197
Sum3337
Variance0.03095542
MonotonicityNot monotonic
2024-03-28T16:15:37.720843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 144928
98.1%
1 2414
 
1.6%
2 328
 
0.2%
3 60
 
< 0.1%
4 13
 
< 0.1%
5 7
 
< 0.1%
ValueCountFrequency (%)
0 144928
98.1%
1 2414
 
1.6%
2 328
 
0.2%
3 60
 
< 0.1%
4 13
 
< 0.1%
5 7
 
< 0.1%
ValueCountFrequency (%)
5 7
 
< 0.1%
4 13
 
< 0.1%
3 60
 
< 0.1%
2 328
 
0.2%
1 2414
 
1.6%
0 144928
98.1%

bicycle_death_count
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147721 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147721
> 99.9%
1 29
 
< 0.1%

Length

2024-03-28T16:15:37.821835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:37.896157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147721
> 99.9%
1 29
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147721
> 99.9%
1 29
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147721
> 99.9%
1 29
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147721
> 99.9%
1 29
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147721
> 99.9%
1 29
 
< 0.1%

bicycle_serious_injury_count
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147487 
1
 
260
2
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147487
99.8%
1 260
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%

Length

2024-03-28T16:15:37.968050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:38.040207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147487
99.8%
1 260
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147487
99.8%
1 260
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147487
99.8%
1 260
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147487
99.8%
1 260
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147487
99.8%
1 260
 
0.2%
2 2
 
< 0.1%
3 1
 
< 0.1%

pedestrian_death_count
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147431 
1
 
317
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147431
99.8%
1 317
 
0.2%
2 2
 
< 0.1%

Length

2024-03-28T16:15:38.120603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:38.458381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147431
99.8%
1 317
 
0.2%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147431
99.8%
1 317
 
0.2%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147431
99.8%
1 317
 
0.2%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147431
99.8%
1 317
 
0.2%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147431
99.8%
1 317
 
0.2%
2 2
 
< 0.1%

pedestrian_serious_injury_count
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147131 
1
 
606
2
 
11
3
 
1
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147131
99.6%
1 606
 
0.4%
2 11
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Length

2024-03-28T16:15:38.545518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:38.643696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147131
99.6%
1 606
 
0.4%
2 11
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147131
99.6%
1 606
 
0.4%
2 11
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147131
99.6%
1 606
 
0.4%
2 11
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147131
99.6%
1 606
 
0.4%
2 11
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147131
99.6%
1 606
 
0.4%
2 11
 
< 0.1%
3 1
 
< 0.1%
9 1
 
< 0.1%

motorcycle_death_count
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147627 
1
 
121
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147627
99.9%
1 121
 
0.1%
2 2
 
< 0.1%

Length

2024-03-28T16:15:38.739379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:38.809962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147627
99.9%
1 121
 
0.1%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147627
99.9%
1 121
 
0.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147627
99.9%
1 121
 
0.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147627
99.9%
1 121
 
0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147627
99.9%
1 121
 
0.1%
2 2
 
< 0.1%

motorcycle_serious_injury_count
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147064 
1
 
662
2
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147064
99.5%
1 662
 
0.4%
2 24
 
< 0.1%

Length

2024-03-28T16:15:38.884518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:38.951221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147064
99.5%
1 662
 
0.4%
2 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147064
99.5%
1 662
 
0.4%
2 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147064
99.5%
1 662
 
0.4%
2 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147064
99.5%
1 662
 
0.4%
2 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147064
99.5%
1 662
 
0.4%
2 24
 
< 0.1%

other_death_count
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147750 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147750
100.0%

Length

2024-03-28T16:15:39.029630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:39.093749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147750
100.0%

Most occurring characters

ValueCountFrequency (%)
0 147750
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147750
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147750
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147750
100.0%

other_serious_injury_count
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147746 
1
 
3
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147746
> 99.9%
1 3
 
< 0.1%
3 1
 
< 0.1%

Length

2024-03-28T16:15:39.161900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:39.228861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147746
> 99.9%
1 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147746
> 99.9%
1 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147746
> 99.9%
1 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147746
> 99.9%
1 3
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147746
> 99.9%
1 3
 
< 0.1%
3 1
 
< 0.1%

onsys_fl
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size144.4 KiB
False
74709 
True
73041 
ValueCountFrequency (%)
False 74709
50.6%
True 73041
49.4%
2024-03-28T16:15:39.290219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

private_dr_fl
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size144.4 KiB
False
147750 
ValueCountFrequency (%)
False 147750
100.0%
2024-03-28T16:15:39.350981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

micromobility_serious_injury_count
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147708 
1
 
40
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147708
> 99.9%
1 40
 
< 0.1%
2 2
 
< 0.1%

Length

2024-03-28T16:15:39.410992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:39.470214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147708
> 99.9%
1 40
 
< 0.1%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147708
> 99.9%
1 40
 
< 0.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147708
> 99.9%
1 40
 
< 0.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147708
> 99.9%
1 40
 
< 0.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147708
> 99.9%
1 40
 
< 0.1%
2 2
 
< 0.1%

micromobility_death_count
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
147744 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters147750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147744
> 99.9%
1 6
 
< 0.1%

Length

2024-03-28T16:15:39.532413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-28T16:15:39.591650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 147744
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 147744
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147744
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 147750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147744
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147744
> 99.9%
1 6
 
< 0.1%

micromobility_fl
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing147439
Missing (%)99.8%
Memory size288.7 KiB
True
 
311
(Missing)
147439 
ValueCountFrequency (%)
True 311
 
0.2%
(Missing) 147439
99.8%
2024-03-28T16:15:39.642682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Interactions

2024-03-28T16:15:19.725777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:00.840134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:02.122963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:03.128760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:04.166154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:05.315236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:06.558818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:07.761033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:08.817481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:09.928514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:11.132401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:12.256316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:13.405591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:14.517256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:15.712674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:16.856981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:18.373094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:19.923338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:01.052424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:02.184815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:03.194142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:04.226930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:05.384258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:06.639125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:07.823989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:08.878369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:09.991363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:11.197015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:12.322905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:13.467375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:14.584243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:15.773478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:16.921217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:18.436253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:20.052734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:01.132578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:02.241432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:03.252935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:04.286379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:05.455036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:06.707292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:07.878133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:08.940430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:10.051543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-03-28T16:15:03.975392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:05.068754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:06.347156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:07.561176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:08.616982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:09.729615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:10.923501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:12.055217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:13.191971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:14.313416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:15.509688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:16.655364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:18.177839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:19.285809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:21.151237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:01.989553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:03.006087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:04.038902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:05.152089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:06.413204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:07.625124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:08.682556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:09.792748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:10.991622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:12.116431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:13.255376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:14.375450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:15.575395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:16.725675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:18.245592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:19.379575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:21.220158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:02.061648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:03.071935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:04.098941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:05.240457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:06.490395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:07.699439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:08.751350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:09.865057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:11.062971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:12.188299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:13.325455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:14.449583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:15.645508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:16.801397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:18.313824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-28T16:15:19.500787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-03-28T16:15:21.484674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-28T16:15:22.212990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

crash_idcrash_fatal_flcrash_datecrash_timecase_idrpt_latituderpt_longituderpt_block_numrpt_street_pfxrpt_street_namerpt_street_sfxcrash_speed_limitroad_constr_zone_fllatitudelongitudestreet_namestreet_nbrstreet_name_2street_nbr_2crash_sev_idsus_serious_injry_cntnonincap_injry_cntposs_injry_cntnon_injry_cntunkn_injry_cnttot_injry_cntdeath_cntcontrib_factr_p1_idcontrib_factr_p2_idunits_involvedatd_mode_category_metadatapedestrian_flmotor_vehicle_flmotorcycle_flbicycle_flother_flpointapd_confirmed_fatalityapd_confirmed_death_countmotor_vehicle_death_countmotor_vehicle_serious_injury_countbicycle_death_countbicycle_serious_injury_countpedestrian_death_countpedestrian_serious_injury_countmotorcycle_death_countmotorcycle_serious_injury_countother_death_countother_serious_injury_countonsys_flprivate_dr_flmicromobility_serious_injury_countmicromobility_death_countmicromobility_fl
013762420N03/30/2014 10:58:00 AM10:58:00140890874NaNNaNNaNNaN3707 MANCHACARD10.0NNaNNaN3707 MANCHACA RDNaNNaNNaN500.00.04.00.00.00NaNNaNPassenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 2259431, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 4, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNNaNN00000000000NN00NaN
113777334N03/27/2014 01:07:00 PM13:07:00140860852NaNNaN3400NaNPALM WAY TO MOPAC NB RAMPNaN50.0N30.404000-97.724450PALM WAY TO MOPAC NB RAMPNaNNaNNaN500.00.01.00.00.00NaNNaNLarge passenger vehicle[{"mode_id": 2, "mode_desc": "Large passenger vehicle", "unit_id": 2260826, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.72445 30.404)N00000000000NN00NaN
213777441N03/28/2014 03:42:00 PM15:42:00140871196NaNNaN8704NaNBALCONES CLUB DRDR-1.0N30.437984-97.788558BALCONES CLUB DR8849.0NaNNaN500.00.01.00.00.00NaNNaNLarge passenger vehicle[{"mode_id": 2, "mode_desc": "Large passenger vehicle", "unit_id": 2260877, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.78855804 30.43798421)N00000000000NN00NaN
313797332N04/09/2014 02:09:00 PM14:09:00140991015NaNNaN8000NaNE US 290 HWY SVRD EBNaN60.0N30.327621-97.662681US0290NaNNaNNaN500.00.01.00.00.00NaNNaNMotor vehicle – other[{"mode_id": 4, "mode_desc": "Motor vehicle \u2013 other", "unit_id": 2262706, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.6626808595204 30.3276207970184)N00000000000YN00NaN
413795604N04/07/2014 06:00:00 PM18:00:00140971248NaNNaN200WBEN WHITEBLVD-1.0N30.225158-97.767199US0290NaNNaNNaN500.00.01.00.00.0074.0NaNPassenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 2262540, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.76719944 30.22515795)N00000000000YN00NaN
513765070N03/31/2014 03:26:00 AM03:26:00140900191NaNNaN8700SIH 35 SVRDNaN50.0N30.169843-97.784252IH0035NaNNaNNaN300.02.00.00.02.00NaNNaNPassenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 2259635, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 2, "non_injry_cnt": 0, "unkn_injry_cnt": 0, "tot_injry_cnt": 2}]NaNYNaNNaNNaNPOINT (-97.78425218 30.16984339)N00000000000YN00NaN
613790426N04/04/2014 02:34:00 PM14:34:00140941160NaNNaN4000NaNS FM 973 RDRD-1.0N30.193205-97.647402FM09734112.0NaNNaN500.00.01.00.00.00NaNNaNPassenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 2261928, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.64740225 30.19320516)N00000000000YN00NaN
713795213N04/18/2014 02:06:00 AM02:06:00141080164NaNNaN1500EANDERSONLN65.0N30.332795-97.686122E ANDERSON LN1500.0UNKNOWNNaN202.00.00.00.02.00NaNNaNLarge passenger vehicle[{"mode_id": 2, "mode_desc": "Large passenger vehicle", "unit_id": 2262511, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 2, "poss_injry_cnt": 0, "non_injry_cnt": 0, "unkn_injry_cnt": 0, "tot_injry_cnt": 2}]NaNYNaNNaNNaNPOINT (-97.68612246 30.33279476)N00000000000NN00NaN
813786430N04/04/2014 12:11:00 AM00:11:00140940020NaNNaN3400NIH 35 NBNaN55.0N30.296133-97.718831IH0035NaNNaNNaN500.00.01.00.00.00NaNNaNPassenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 2261651, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.71883142 30.29613303)N00000000000YN00NaN
913792606N04/18/2014 10:05:00 AM10:05:00141080445NaNNaN700SMOPACEXPY65.0N30.265780-97.782539SL0001NaNNaNNaN500.00.02.00.00.00NaNNaNPassenger car & Passenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 2262335, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}, {"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 2262347, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.78253937 30.2657795)N00000000000YN00NaN
crash_idcrash_fatal_flcrash_datecrash_timecase_idrpt_latituderpt_longituderpt_block_numrpt_street_pfxrpt_street_namerpt_street_sfxcrash_speed_limitroad_constr_zone_fllatitudelongitudestreet_namestreet_nbrstreet_name_2street_nbr_2crash_sev_idsus_serious_injry_cntnonincap_injry_cntposs_injry_cntnon_injry_cntunkn_injry_cnttot_injry_cntdeath_cntcontrib_factr_p1_idcontrib_factr_p2_idunits_involvedatd_mode_category_metadatapedestrian_flmotor_vehicle_flmotorcycle_flbicycle_flother_flpointapd_confirmed_fatalityapd_confirmed_death_countmotor_vehicle_death_countmotor_vehicle_serious_injury_countbicycle_death_countbicycle_serious_injury_countpedestrian_death_countpedestrian_serious_injury_countmotorcycle_death_countmotorcycle_serious_injury_countother_death_countother_serious_injury_countonsys_flprivate_dr_flmicromobility_serious_injury_countmicromobility_death_countmicromobility_fl
14774020083275N03/07/2024 02:00:00 PM14:00:00240670719NaNNaN4900.0NaNLOCAL RDNaN35.0NNaNNaNLOCAL RDNaNNaNNaN000.00.00.00.00.00NaNNaNPassenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 4820252, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 0, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNNaNN00000000000NN00NaN
14774120061720N03/06/2024 07:35:00 AM07:35:00240660301NaNNaN6300.0NaNMOPACEXPY65.0N30.337796-97.755030SL0001NaNNaNNaN300.01.00.00.01.00NaNNaNOther/Unknown & Motorcycle[{"mode_id": 9, "mode_desc": "Other/Unknown", "unit_id": 4817211, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 0, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}, {"mode_id": 3, "mode_desc": "Motorcycle", "unit_id": 4817452, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 1, "non_injry_cnt": 0, "unkn_injry_cnt": 0, "tot_injry_cnt": 1}]NaNNaNYNaNYPOINT (-97.75503017 30.33779555)N00000000000YN00NaN
14774220048732N02/27/2024 05:12:00 PM17:12:0024058068430.43730-97.66906NaNNaNIH 35NaN65.0N30.437301-97.669063IH0035NaNNaNNaN300.01.01.00.01.00NaNNaNOther/Unknown & Large passenger vehicle & Large passenger vehicle[{"mode_id": 9, "mode_desc": "Other/Unknown", "unit_id": 4815130, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 0, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}, {"mode_id": 2, "mode_desc": "Large passenger vehicle", "unit_id": 4815237, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 1, "non_injry_cnt": 0, "unkn_injry_cnt": 0, "tot_injry_cnt": 1}, {"mode_id": 2, "mode_desc": "Large passenger vehicle", "unit_id": 4815427, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNYPOINT (-97.66906333 30.43730062)N00000000000YN00NaN
14774320082999N03/06/2024 03:32:00 PM15:32:0024066075530.18018-97.79516NaNNaNS 1ST STNaN45.0N30.230698-97.766790S 1ST ST8304.0UNKNOWNNaN500.00.01.00.00.0020.0NaNPassenger car & Passenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 4819400, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 0, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}, {"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 4819554, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.76679002542313 30.230698003304788)N00000000000NN00NaN
14774420062080N03/06/2024 07:31:00 AM07:31:0024066029530.34600-97.71000600.0WE ANDERSON LN SVRD WBLN50.0N30.347375-97.712073US0183NaNNaNNaN500.00.02.00.00.00NaNNaNLarge passenger vehicle & Passenger car[{"mode_id": 2, "mode_desc": "Large passenger vehicle", "unit_id": 4817933, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}, {"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 4817934, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.71207312 30.34737489)N00000000000YN00NaN
14774520060069N03/05/2024 03:23:00 AM03:23:00240650140NaNNaN8800.0NaNN IH 35 NBNaN70.0Y30.351808-97.692109IH0035NaNNaNNaN500.00.01.00.00.00NaNNaNLarge passenger vehicle[{"mode_id": 2, "mode_desc": "Large passenger vehicle", "unit_id": 4816540, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.69210894 30.35180792)N00000000000YN00NaN
14774620056192N03/01/2024 09:28:00 PM21:28:0024061142030.34404-97.711447635.0NGUADALUPE STST25.0N30.344697-97.710384GUADALUPE ST7834.0NaNNaN500.00.01.00.00.00NaNNaNPassenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 4816315, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.71038439 30.34469722)N00000000000NN00NaN
14774720083436N03/10/2024 12:16:00 AM00:16:00240700030NaNNaN3500.0NaNMONTOPOLIS DRDR35.0N30.208255-97.714922MONTOPOLIS DRNaNTRADE CENTER DRNaN500.00.03.00.00.00NaNNaNPassenger car & Passenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 4819796, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 2, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}, {"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 4819797, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.71492247 30.20825479)N00000000000NN00NaN
14774820049322N02/28/2024 01:27:00 AM01:27:00240590075NaNNaN3800.0NaNSPICEWOOD SPRINGS RD EBRD35.0N30.364563-97.749548SPICEWOOD SPRINGS RD3800.0NaNNaN500.00.02.00.00.00NaNNaNPassenger car[{"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 4815440, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 2, "unkn_injry_cnt": 0, "tot_injry_cnt": 0}]NaNYNaNNaNNaNPOINT (-97.74954801 30.36456254)N00000000000NN00NaN
14774920047391N02/22/2024 07:57:00 PM19:57:0024053138330.15390-97.7921910100.0SS IH 35 SVRD SBNaN55.0N30.153733-97.791369IH0035NaNNaNNaN201.00.01.01.01.00NaNNaNMotor vehicle – other & Passenger car[{"mode_id": 4, "mode_desc": "Motor vehicle \u2013 other", "unit_id": 4814685, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 0, "poss_injry_cnt": 0, "non_injry_cnt": 0, "unkn_injry_cnt": 1, "tot_injry_cnt": 0}, {"mode_id": 1, "mode_desc": "Passenger car", "unit_id": 4814686, "death_cnt": 0, "sus_serious_injry_cnt": 0, "nonincap_injry_cnt": 1, "poss_injry_cnt": 0, "non_injry_cnt": 1, "unkn_injry_cnt": 0, "tot_injry_cnt": 1}]NaNYNaNNaNNaNPOINT (-97.79136896 30.15373339)N00000000000YN00NaN